/*************************************************************************
Copyright (c) 2007-2008, Sergey Bochkanov (ALGLIB project).

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:

- Redistributions of source code must retain the above copyright
  notice, this list of conditions and the following disclaimer.

- Redistributions in binary form must reproduce the above copyright
  notice, this list of conditions and the following disclaimer listed
  in this license in the documentation and/or other materials
  provided with the distribution.

- Neither the name of the copyright holders nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*************************************************************************/

#ifndef _linreg_h
#define _linreg_h

#include "ap.h"
#include "ialglib.h"

#include "descriptivestatistics.h"
#include "gammaf.h"
#include "normaldistr.h"
#include "igammaf.h"
#include "reflections.h"
#include "bidiagonal.h"
#include "qr.h"
#include "lq.h"
#include "blas.h"
#include "rotations.h"
#include "bdsvd.h"
#include "svd.h"


struct linearmodel
{
    ap::real_1d_array w;
};
struct lrreport
{
    ap::real_2d_array c;
    double rmserror;
    double avgerror;
    double avgrelerror;
    double cvrmserror;
    double cvavgerror;
    double cvavgrelerror;
    int ncvdefects;
    ap::integer_1d_array cvdefects;
};


/*************************************************************************
Linear regression

Subroutine builds model:

    Y = A(0)*X[0] + ... + A(N-1)*X[N-1] + A(N)

and model found in ALGLIB format, covariation matrix, training set  errors
(rms,  average,  average  relative)   and  leave-one-out  cross-validation
estimate of the generalization error. CV  estimate calculated  using  fast
algorithm with O(NPoints*NVars) complexity.

When  covariation  matrix  is  calculated  standard deviations of function
values are assumed to be equal to RMS error on the training set.

INPUT PARAMETERS:
    XY          -   training set, array [0..NPoints-1,0..NVars]:
                    * NVars columns - independent variables
                    * last column - dependent variable
    NPoints     -   training set size, NPoints>NVars+1
    NVars       -   number of independent variables

OUTPUT PARAMETERS:
    Info        -   return code:
                    * -255, in case of unknown internal error
                    * -4, if internal SVD subroutine haven't converged
                    * -1, if incorrect parameters was passed (NPoints<NVars+2, NVars<1).
                    *  1, if subroutine successfully finished
    LM          -   linear model in the ALGLIB format. Use subroutines of
                    this unit to work with the model.
    AR          -   additional results


  -- ALGLIB --
     Copyright 02.08.2008 by Bochkanov Sergey
*************************************************************************/
void lrbuild(const ap::real_2d_array& xy,
     int npoints,
     int nvars,
     int& info,
     linearmodel& lm,
     lrreport& ar);


/*************************************************************************
Linear regression

Variant of LRBuild which uses vector of standatd deviations (errors in
function values).

INPUT PARAMETERS:
    XY          -   training set, array [0..NPoints-1,0..NVars]:
                    * NVars columns - independent variables
                    * last column - dependent variable
    S           -   standard deviations (errors in function values)
                    array[0..NPoints-1], S[i]>0.
    NPoints     -   training set size, NPoints>NVars+1
    NVars       -   number of independent variables

OUTPUT PARAMETERS:
    Info        -   return code:
                    * -255, in case of unknown internal error
                    * -4, if internal SVD subroutine haven't converged
                    * -1, if incorrect parameters was passed (NPoints<NVars+2, NVars<1).
                    * -2, if S[I]<=0
                    *  1, if subroutine successfully finished
    LM          -   linear model in the ALGLIB format. Use subroutines of
                    this unit to work with the model.
    AR          -   additional results


  -- ALGLIB --
     Copyright 02.08.2008 by Bochkanov Sergey
*************************************************************************/
void lrbuilds(const ap::real_2d_array& xy,
     const ap::real_1d_array& s,
     int npoints,
     int nvars,
     int& info,
     linearmodel& lm,
     lrreport& ar);


/*************************************************************************
Like LRBuildS, but builds model

    Y = A(0)*X[0] + ... + A(N-1)*X[N-1]

i.e. with zero constant term.

  -- ALGLIB --
     Copyright 30.10.2008 by Bochkanov Sergey
*************************************************************************/
void lrbuildzs(const ap::real_2d_array& xy,
     const ap::real_1d_array& s,
     int npoints,
     int nvars,
     int& info,
     linearmodel& lm,
     lrreport& ar);


/*************************************************************************
Like LRBuild but builds model

    Y = A(0)*X[0] + ... + A(N-1)*X[N-1]

i.e. with zero constant term.

  -- ALGLIB --
     Copyright 30.10.2008 by Bochkanov Sergey
*************************************************************************/
void lrbuildz(const ap::real_2d_array& xy,
     int npoints,
     int nvars,
     int& info,
     linearmodel& lm,
     lrreport& ar);


/*************************************************************************
Unpacks coefficients of linear model.

INPUT PARAMETERS:
    LM          -   linear model in ALGLIB format

OUTPUT PARAMETERS:
    V           -   coefficients, array[0..NVars]
    NVars       -   number of independent variables (one less than number
                    of coefficients)

  -- ALGLIB --
     Copyright 30.08.2008 by Bochkanov Sergey
*************************************************************************/
void lrunpack(const linearmodel& lm, ap::real_1d_array& v, int& nvars);


/*************************************************************************
"Packs" coefficients and creates linear model in ALGLIB format (LRUnpack
reversed).

INPUT PARAMETERS:
    V           -   coefficients, array[0..NVars]
    NVars       -   number of independent variables

OUTPUT PAREMETERS:
    LM          -   linear model.

  -- ALGLIB --
     Copyright 30.08.2008 by Bochkanov Sergey
*************************************************************************/
void lrpack(const ap::real_1d_array& v, int nvars, linearmodel& lm);


/*************************************************************************
Procesing

INPUT PARAMETERS:
    LM      -   linear model
    X       -   input vector,  array[0..NVars-1].

Result:
    value of linear model regression estimate

  -- ALGLIB --
     Copyright 03.09.2008 by Bochkanov Sergey
*************************************************************************/
double lrprocess(const linearmodel& lm, const ap::real_1d_array& x);


/*************************************************************************
RMS error on the test set

INPUT PARAMETERS:
    LM      -   linear model
    XY      -   test set
    NPoints -   test set size

RESULT:
    root mean square error.

  -- ALGLIB --
     Copyright 30.08.2008 by Bochkanov Sergey
*************************************************************************/
double lrrmserror(const linearmodel& lm,
     const ap::real_2d_array& xy,
     int npoints);


/*************************************************************************
Average error on the test set

INPUT PARAMETERS:
    LM      -   linear model
    XY      -   test set
    NPoints -   test set size

RESULT:
    average error.

  -- ALGLIB --
     Copyright 30.08.2008 by Bochkanov Sergey
*************************************************************************/
double lravgerror(const linearmodel& lm,
     const ap::real_2d_array& xy,
     int npoints);


/*************************************************************************
RMS error on the test set

INPUT PARAMETERS:
    LM      -   linear model
    XY      -   test set
    NPoints -   test set size

RESULT:
    average relative error.

  -- ALGLIB --
     Copyright 30.08.2008 by Bochkanov Sergey
*************************************************************************/
double lravgrelerror(const linearmodel& lm,
     const ap::real_2d_array& xy,
     int npoints);


/*************************************************************************
Copying of LinearModel strucure

INPUT PARAMETERS:
    LM1 -   original

OUTPUT PARAMETERS:
    LM2 -   copy

  -- ALGLIB --
     Copyright 15.03.2009 by Bochkanov Sergey
*************************************************************************/
void lrcopy(const linearmodel& lm1, linearmodel& lm2);


/*************************************************************************
Serialization of LinearModel strucure

INPUT PARAMETERS:
    LM      -   original

OUTPUT PARAMETERS:
    RA      -   array of real numbers which stores model,
                array[0..RLen-1]
    RLen    -   RA lenght

  -- ALGLIB --
     Copyright 15.03.2009 by Bochkanov Sergey
*************************************************************************/
void lrserialize(const linearmodel& lm, ap::real_1d_array& ra, int& rlen);


/*************************************************************************
Unserialization of DecisionForest strucure

INPUT PARAMETERS:
    RA      -   real array which stores decision forest

OUTPUT PARAMETERS:
    LM      -   unserialized structure

  -- ALGLIB --
     Copyright 15.03.2009 by Bochkanov Sergey
*************************************************************************/
void lrunserialize(const ap::real_1d_array& ra, linearmodel& lm);


/*************************************************************************
Obsolete subroutine, use LRBuildS

  -- ALGLIB --
     Copyright 26.04.2008 by Bochkanov Sergey

References:
1. Numerical Recipes in C, "15.2 Fitting Data to a Straight Line"
*************************************************************************/
void lrlines(const ap::real_2d_array& xy,
     const ap::real_1d_array& s,
     int n,
     int& info,
     double& a,
     double& b,
     double& vara,
     double& varb,
     double& covab,
     double& corrab,
     double& p);


/*************************************************************************
Obsolete subroutine, use LRBuild

  -- ALGLIB --
     Copyright 02.08.2008 by Bochkanov Sergey
*************************************************************************/
void lrline(const ap::real_2d_array& xy,
     int n,
     int& info,
     double& a,
     double& b);


#endif
